Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Pain Medicine

Background:

• Traditional pain assessment relies on subjective reporting or observation, which can be unreliable.
• Automatic pain assessment is needed for individuals with impaired reporting abilities, like athletes.
• Existing deep learning methods for pain assessment require large datasets and struggle with generalization.

Purpose of the Study:

• To develop novel multi-modal deep learning approaches for objective pain intensity assessment.
• To address data scarcity and generalization issues in pain assessment models.
• To improve the efficiency and accuracy of automated pain detection systems.

Main Methods:

• Implementation of multi-modal deep learning, incorporating supervised and self-supervised learning techniques.
• Utilizing measurable bio-physiological data for pain intensity prediction.
• Developing a self-supervised approach for automatic data generation and model fine-tuning.

Main Results:

• The supervised deep learning approach achieved state-of-the-art inference performance.
• The self-supervised approach significantly improved data efficiency by leveraging automatically generated physiological data.
• The model demonstrated effective fine-tuning on smaller datasets.

Conclusions:

• Novel multi-modal deep learning methods offer a promising solution for objective pain assessment.
• Self-supervised learning enhances data efficiency, making models more practical for real-world pain intensity prediction.
• These advancements are crucial for applications where traditional pain assessment is challenging.